| PGNMF {hNMF} | R Documentation |
NMF by alternating non-negative least squares using projected gradients. For a reference to the method, see C.-J. Lin, "Projected Gradient Methods for Non-negative Matrix Factorization", Neural computation 19.10 (2007): 2756-2779.
Description
NMF by alternating non-negative least squares using projected gradients. For a reference to the method, see C.-J. Lin, "Projected Gradient Methods for Non-negative Matrix Factorization", Neural computation 19.10 (2007): 2756-2779.
Usage
PGNMF(X, nmfMod, tol = 1e-05, maxIter = 500, timeLimit = 300,
checkDivergence = TRUE)
Arguments
X |
Input data matrix, each column represents one data point and the rows correspond to the different features |
nmfMod |
Valid NMF model, containing initialized factor matrices (in accordance with the NMF package definition) |
tol |
Tolerance for a relative stopping condition |
maxIter |
Maximum number of iterations |
timeLimit |
Limit of time duration NMF analysis |
checkDivergence |
Boolean indicating whether divergence checking should be performed Default is TRUE, but it should be set to FALSE when using random initialization |
Value
Resulting NMF model (in accordance with the NMF package definition)
Author(s)
nsauwen